#Setup

library(Seurat)
library(tidyverse)
library(scales)
library(viridis)
library(patchwork)
library(ggrepel)



#set working directory to the location of the file
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

source("../my_functions.R") #Loading a couple of custom functions for plotting

P30 Only ——————————-

b_cells <- read.table('b_cells.txt') %>% pull(V1)

#-Subset B cell clusters

#Idents(Neurogenic_lineage) = "SCT_snn_res.2"
Head <- subset(integrated_exp, cells = b_cells)
Head <- Head %>% 
        SCTransform() %>% 
        RunPCA(assay = "SCT", npcs = 100) %>% 
        IntegrateLayers(method               = CCAIntegration, 
                        normalization.method = "SCT", 
                        verbose              = F) %>% 
        RunUMAP(dims = 1:100, reduction = "integrated.dr") %>% 
        FindNeighbors(reduction = "integrated.dr", dims = 1:100) %>% 
        FindClusters(resolution = seq(0.5, 2, 0.5), graph.name = "SCT_snn")

DimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1") + 
  NoLegend()  + 
  coord_fixed() + 
  NoAxes()

Rotating the UMAP coordinates

umap_original <- Head@reductions$umap@cell.embeddings

# Custom function to create a 2D rotation matrix
rot2 <- function(angle_radians) {
  matrix(c(cos(angle_radians), -sin(angle_radians),
           sin(angle_radians), cos(angle_radians)), nrow = 2)
}

# Function to rotate multiple points using a matrix of coordinates
rotate_points <- function(points, angle_degrees) {
  # Convert the angle from degrees to radians
  angle_radians <- angle_degrees * pi / 180
  
  # Get the rotation matrix
  rotation_matrix <- rot2(angle_radians)
  
  # Apply the rotation matrix to the matrix of points (transpose for correct multiplication)
  rotated_points <- t(rotation_matrix %*% t(points))
  
  return(rotated_points)
}


# Rotate all points by 45 degrees
umap_rotated <- rotate_points(umap_original, 90)
colnames(umap_rotated) <- c("umap_1", "umap_2")
Head@reductions$umap@cell.embeddings <- umap_rotated
DimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1") + 
  NoLegend()  + 
  coord_fixed() + 
  NoAxes()

Nearest Neightbors Analysis to Identify B1 cells

DimPlot(Head, group.by = c("cell_type_label", "region")) & NoAxes() & coord_fixed()

FeaturePlot(Head, "Wpre", order = T) + scale_color_viridis(option = "A")

####-Identifying Tdtomato+ (Wpre+) cells

VlnPlot(Head, "Wpre", group.by = "batch", split.by = "region") #There is one cell with Wpre > 2 in the Wedge region. This cell could be a contaminant or a B1 cell that transitioned to B2 between injection and cell dissociation.
wpre.data %>% filter(batch == "batch_2" & region == 'LW') %>% pull(wpre_expression) %>% quantile(probs = 0.875)
   87.5% 
1.098612 
#Selecting Wpre+ cells, defined as cells with normalized Wpre expression > 1.39, excluding cells coming from the Wedge region.
wpre.data <- wpre.data %>% 
  mutate(wpre_label = case_when(batch == "batch_2" & 
                                  wpre_expression > batch2_thres & 
                                  region!= "Wedge" ~ "wpre+",
                                batch == "batch_3" &
                                  wpre_expression > batch3_thres &
                                region != "Wedge" ~ "wpre+",
                                .default = "wpre-"))

#Calculating the number of unlabeled B1 cells in the dataset:
wpre.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, wpre_label) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)

####-Identifying non-infected B1 cells

#Assuming that these cells are the most similar to Wpre+ cells.
#Splitting our dataset into different batches, selecting only cells that are not in the Wedge region.
wpre_cells_batch2 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_2") %>% 
                      rownames()

wpre_cells_batch3 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_3") %>% 
                      rownames()
nonwedge_batch2_cells <- Head@meta.data %>% filter(region != "Wedge" & batch == "batch_2") %>% rownames()
nonwedge_batch3_cells <- Head@meta.data %>% filter(region != "Wedge" & batch == "batch_3") %>% rownames()

Head_batch2 <- Head %>% subset(cells = nonwedge_batch2_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:100, 
                k.param = 30,
                return.neighbor = T)

Head_batch3 <- Head %>% subset(cells = nonwedge_batch3_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:100, 
                k.param = 30, 
                return.neighbor = T)
#Inferring unlabeled b1 cells in batch 2:
wpre_index_batch2 <- which(Head_batch2@neighbors$SCT.nn@cell.names %in% wpre_cells_batch2)

good.neighbors_batch2 <- c()
bad.neighbors <- wpre_index_batch2

for (i in wpre_index_batch2) {
  all.neighbors <- Head_batch2@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch2 <- c(good.neighbors_batch2, 
                             (all.neighbors[!all.neighbors %in% bad.neighbors])[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch2) %>% unique()
}

wpre_neighbors_batch2 <- Head_batch2@neighbors$SCT.nn@cell.names[good.neighbors_batch2]
wpre_neighbors_batch2 <- wpre_neighbors_batch2[!is.na(wpre_neighbors_batch2)]

#Inferring unlabeled b1 cells in batch 3:
wpre_index_batch3 <- which(Head_batch3@neighbors$SCT.nn@cell.names %in% wpre_cells_batch3)

good.neighbors_batch3 <- c()
bad.neighbors <- wpre_index_batch3

for (i in wpre_index_batch3) {
  all.neighbors <- Head_batch3@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch3 <- c(good.neighbors_batch3, (setdiff(all.neighbors, bad.neighbors))[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch3)
}

wpre_neighbors_batch3 <- Head_batch3@neighbors$SCT.nn@cell.names[good.neighbors_batch3]
wpre_neighbors_batch3 <- wpre_neighbors_batch3[!is.na(wpre_neighbors_batch3)]
wpre.data[, "b_type"] <- "b2"
wpre.data[c(wpre_neighbors_batch2, wpre_neighbors_batch3),"b_type"] <- "b1 - nn"
wpre.data[c(wpre_cells_batch2, wpre_cells_batch3), "b_type"] <- "b1 - labeled"

wpre.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, b_type) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
Head@meta.data[, "b_type"] <- "b2"
Head@meta.data[c(wpre_neighbors_batch2, wpre_neighbors_batch3),"b_type"] <- "b1 - nn"
Head@meta.data[c(wpre_cells_batch2, wpre_cells_batch3), "b_type"] <- "b1 - labeled"

DimPlot(Head, group.by = "b_type", shuffle = T) & NoAxes() & coord_fixed()
DimPlot(Head, group.by = "b_type", split.by = "b_type") & NoAxes() & coord_fixed()

####-Label transfer to uninfected cells

Head@active.assay = "RNA"

non_infected_features <- rownames(Head)[!rownames(Head) %in% c('Wpre', 'GFP', 'Cre')]

Head_batch1 <- subset(Head, subset = batch == "batch_1", features = non_infected_features) %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20) %>%
  FindNeighbors(dims = 1:20)


Head_batch23 <- subset(Head, subset = batch %in% c("batch_2", "batch_3"))
Head_batch23 <- Head_batch23 %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20)
Head_batch23 <- IntegrateLayers(Head_batch23, method = CCAIntegration, orig.reduction = "pca", dims = 1:20)

Head.anchors <- Head_batch23 %>%
  FindTransferAnchors(reference = .,
                      query = Head_batch1,
                      dims = 1:20,
                      reference.reduction = "pca")

predictions <- TransferData(anchorset = Head.anchors, refdata = Head_batch23$b_type, dims = 1:20)

inferredb1_batch1_cells <- predictions %>% filter(predicted.id != "b2") %>% rownames()
Head@meta.data[inferredb1_batch1_cells, "b_type"] <- "b1 - nn"
Head@meta.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, b_type) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
FeaturePlot(Head, c("S100a6", "Egfr", "Crym", "Urah", "Tfap2c"), order = T, ncol = 5) & 
  scale_color_viridis(option = "A") & 
  coord_fixed() & simple


Idents(Head) = "SCT_snn_res.1"

Head@meta.data <- Head@meta.data %>%
  mutate(cell_subtype = case_when(
    SCT_snn_res.1 %in% c(4, 2, 8, 11, 10) ~ "aB cells",
    SCT_snn_res.1 == 0 ~ "Ventral Subpallial B cells",
    SCT_snn_res.1 %in% c(3, 5) ~ "Dorsal Pallial B cells",
    SCT_snn_res.1 %in% c(6, 1, 9, 7) ~ "Dorsal Subpallial B cells",
    TRUE ~ "Unknown"
  ))

Head@meta.data <- Head@meta.data %>%
  mutate(activation_state = case_when(
    cell_subtype == "aB cells" ~ "activated",
    TRUE ~ "quiescent"
  ))

Idents(Head) = "SCT_snn_res.1"
DimPlot(Head, 
        group.by = "cell_subtype", 
        label=F, 
        cols = c("cyan","blue","dodgerblue2","dodgerblue4", "dodgerblue3")) +
  NoLegend() + 
  coord_fixed() + 
  NoAxes()

DimPlot(Head, group.by ="region", label = F, shuffle = F, cols = c("grey", "purple", "dodgerblue2"), pt.size = 1.5) & NoAxes() & coord_fixed()

#B1 score -----------------------------
Head@active.assay <- "SCT"
b.markers.list = list(b1 = c("Atf3", "Riiad1", "Foxj1", "Gadd45b", "Tagln2", "Emp1"))

Head = AddModuleScore(Head, features = b.markers.list)
Head@meta.data  = Head@meta.data %>% dplyr::rename(B1_score = Cluster1)

#Re-scaling Module Scores:These lines rescale the module scores (B1_score and B2_score) so that they have a mean of 0 and a standard deviation of 1. This step standardizes the scores, making them comparable across different modules.

Head$B1_score = Head$B1_score %>% rescale() 

FeaturePlot(Head, c("B1_score"), order = T, pt.size = 1.5) & 
  scale_color_viridis(option = "magma") & 
  NoAxes() & 
  coord_fixed()

saveRDS(Head, "data/Head.rds")

P30+P365 ——————————-

integrated_exp <- readRDS("../pre-processing/integrated_exp_withp365.rds")
mDimPlot(integrated_exp, group.by = "cell_type", label = T, shuffle = T, legend = F, repel = T)

-Subset B clusters

b_cells <- integrated_exp@meta.data %>% filter(cell_type == 'B cells') %>% rownames()
Head <- subset(integrated_exp, cells = b_cells)
Head <- Head %>% 
        SCTransform() %>% 
        RunPCA(assay = "SCT", npcs = 100) %>% 
        IntegrateLayers(method               = CCAIntegration, 
                        normalization.method = "SCT", 
                        verbose              = F) %>% 
        RunUMAP(dims = 1:100, reduction = "integrated.dr") %>% 
        FindNeighbors(reduction = "integrated.dr", dims = 1:100) %>% 
        FindClusters(resolution = seq(0.5, 2, 0.5), graph.name = "SCT_snn")
Running SCTransform on assay: RNA
Running SCTransform on layer: counts.1
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 18667 by 3092
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 3092 cells
Found 67 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 18667 genes
Computing corrected count matrix for 18667 genes
Calculating gene attributes
Wall clock passed: Time difference of 40.20527 secs
Determine variable features
Centering data matrix

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Running SCTransform on layer: counts.2
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 16588 by 520
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 520 cells
Found 19 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 16588 genes
Computing corrected count matrix for 16588 genes
Calculating gene attributes
Wall clock passed: Time difference of 10.79082 secs
Determine variable features
Centering data matrix

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Running SCTransform on layer: counts.3
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 17437 by 2293
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 2293 cells
Found 28 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 17437 genes
Computing corrected count matrix for 17437 genes
Calculating gene attributes
Wall clock passed: Time difference of 25.02378 secs
Determine variable features
Centering data matrix

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Centering data matrix

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Warning: Different cells and/or features from existing assay SCTSet default assay to SCT
Warning: The following 64 features requested have not been scaled (running reduction without them): Ifi27l2a, GFP, Cre, Lcn2, C3, Wpre, Ifi44, Gm42047, H2-Aa, Phf11d, Cfb, H2-Eb1, Slfn8, Tgm1, Gm11728, AW112010, BC028528, Olfr1369-ps1, Fam216b, Gm10457, 4930477G07Rik, Cxcl5, Ssc4d, Shoc1, Oasl1, Oas1a, Tex21, Tgtp2, Hs3st4, Mlkl, Wfdc2, Apol9a, Cxcl9, Zyg11a, Myoc, C2cd4b, Gm10636, Tbx20, Actn3, Gm13538, Gm13219, Cdcp1, Ccl5, Btbd16, Acod1, Npffr1, Scgb1c1, Gm16090, Atp12a, Syce3, Trarg1, Gm29773, Ccl19, Sfta2, Ifi204, 4930445E18Rik, Gm32913, Slc28a1, Tbc1d30, 4732419C18Rik, Gm12185, Pde6c, Fgf23, Slc44a4PC_ 1 
Positive:  Egfr, Nrxn3, Kcnh7, Gm29260, Tmsb4x, Slit2, Ccnd2, Fabp7, Ptprz1, Lrrc7 
       Pak3, Nol4, Sox11, Nrxn1, Dscaml1, Zbtb20, Setbp1, Sv2c, Sox2ot, Tcf12 
       Slc4a7, Mir99ahg, Zeb1, Msi2, Nrg1, Marcks, Map2, Sox4, Auts2, Shc3 
Negative:  Mt1, Junb, Klf2, Clu, Crym, Cebpd, Mt3, Gfap, S100a6, Id3 
       Gadd45g, Zfp36, Atf3, Ccn1, Fth1, Cebpb, Cst3, Cxcl10, Ier2, Fxyd1 
       Mt2, Btg2, Apoe, Aldoc, Prdx6, Dusp1, Klf4, Ifitm3, Fos, Rgcc 
PC_ 2 
Positive:  Crym, Klf2, Cxcl10, Atf3, Dnajb1, Rsad2, Cebpd, Egr3, Isg15, Emp1 
       Klf6, Jun, Egr2, Klf4, Gbp2, Fas, Zfp36, Serpina3n, Nfatc2, Arc 
       H2-K1, Ddit3, Actb, Ncam2, Junb, Prss56, Ier2, Homer1, Btg2, Slit2 
Negative:  Nrg1, Pla2g7, Sparcl1, Mt1, Clu, Aldoc, Thbs4, Ntrk2, Gpc5, Gfap 
       Igfbp5, Gm29260, Gabrb1, Dcc, Mdga2, Slc15a2, Pax6, Fxyd6, Cst3, Rmst 
       Cdk11b, Cldn10, Slc1a2, Lama2, Lrp4, Acsl3, Cadm2, Apoe, Fbxo2, Gm42418 
PC_ 3 
Positive:  Hes5, Btg2, Homer1, Jun, Fos, Ier2, Cdk11b, Pde10a, Jund, Junb 
       Hes1, Trpm3, Dnajb1, Klf4, Egr1, Arih1, Nr4a3, Nrg1, Nr4a1, Dusp1 
       Vps37b, Ddit3, Fosb, Gpc5, Urah, Usp2, Coq10b, Gm39325, Nkain2, Maff 
Negative:  Mt1, Gfap, Basp1, Nnat, Tmsb10, Sparcl1, Dlx6os1, Sox2ot, Stmn2, Nav3 
       Aqp4, S100a6, Nrxn3, Clu, Tubb3, Ifitm3, Mt3, Mt2, Stmn1, Dcx 
       Gm42418, Fth1, Celf4, Tuba1a, Cebpd, Igfbpl1, Lrrc7, Serpina3n, Tubb5, Adarb2 
PC_ 4 
Positive:  Crym, Frmd4a, Klf2, Grm3, Nrxn1, Ntm, Ncam2, Dpyd, Ptprt, Gabrb1 
       Adamts18, Lsamp, Mir99ahg, Prex2, Grid2, Lrrc4c, B3galt1, Slit2, Cadm2, Sgcd 
       Rora, Adgrl3, Hdac9, Negr1, Pcdh9, Fars2, Cebpd, Prss56, Slc1a2, Zbtb20 
Negative:  Cdk11b, Pde10a, Gfap, Vps37b, Tmsb4x, Emd, Mt1, Slc25a3, Urah, Arih1 
       Dad1, Rps24, Tubb5, Nop53, Rpl13, Snhg15, Cebpb, Rpsa, Rplp1, Trmt61b 
       Ccnd2, Rpl32, Rps8, Maff, Rpl23, Rps27a, Nr4a2, Nrxn3, Rplp0, Nme2 
PC_ 5 
Positive:  Dlx6os1, Nav3, Stmn2, Nrxn3, Sox2ot, Tubb3, Dcx, Basp1, Celf4, Adarb2 
       Erbb4, Gm38505, Igfbpl1, Gad2, Cacna1c, Tmsb10, Junb, Anks1b, Shtn1, Sp9 
       Btg2, Enox1, Plcl1, 2610307P16Rik, Myt1l, Map1b, Nol4, Hes5, Lrrc7, Ncam1 
Negative:  Fabp7, Egfr, Sv2c, Rpsa, Rplp1, Rps8, Rpl13, Nme2, Rplp0, Rpl32 
       Rpl39, Lrrfip1, Shc3, Rpl23, Rps24, Rps27a, Rps12, Slc4a7, Emp1, Ptprz1 
       Tmsb4x, Eef1a1, Ptx3, Gm29260, Hsp90ab1, Pvt1, Ankrd28, Tnfrsf12a, Rpl15, Rorb 

  |                                                  | 0 % ~calculating  
  |+++++++++++++++++                                 | 33% ~00s          
  |++++++++++++++++++++++++++++++++++                | 67% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  

  |                                                  | 0 % ~calculating  
  |+++++++++++++++++                                 | 33% ~09s          
  |++++++++++++++++++++++++++++++++++                | 67% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s  
13:06:11 UMAP embedding parameters a = 0.9922 b = 1.112
13:06:11 Read 5905 rows and found 100 numeric columns
13:06:11 Using Annoy for neighbor search, n_neighbors = 30
13:06:11 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:06:12 Writing NN index file to temp file /tmp/RtmpvrQ0a0/file1b8b1f474deaf1
13:06:12 Searching Annoy index using 1 thread, search_k = 3000
13:06:14 Annoy recall = 100%
13:06:19 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
13:06:26 Initializing from normalized Laplacian + noise (using RSpectra)
13:06:26 Commencing optimization for 500 epochs, with 263284 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:06:36 Optimization finished
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5905
Number of edges: 415420

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8226
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5905
Number of edges: 415420

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7517
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5905
Number of edges: 415420

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7033
Number of communities: 15
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5905
Number of edges: 415420

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6588
Number of communities: 16
Elapsed time: 0 seconds
mDimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1", legend = F)

Rotating the UMAP coordinates

umap_original <- Head@reductions$umap@cell.embeddings

# Rotate all points by 45 degrees
umap_rotated <- rotate_points(umap_original, 180)
colnames(umap_rotated) <- c("umap_1", "umap_2")
Head@reductions$umap@cell.embeddings <- umap_rotated

mDimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.0.5", legend = F)

Assigning regions

#- B1 / B2 identification ### Identifying Tdtomato+ (Wpre+) cells

#Assuming that B1 and B2 cells co-exist in similar numbers at P30, and only 25% of B1 cells are infected, we should select the top 12.5% Wpre-expressing cells in each batch.
wpre.data = Head@meta.data %>% 
  mutate(wpre_expression = Head@assays$SCT@data["Wpre",] %>% 
         as.data.frame() %>% 
         pull("."))

batch2_thres <- wpre.data %>% 
  filter(batch == "batch_2" & 
         region == 'LW' & 
         age == 'p30') %>% 
  pull(wpre_expression) %>% 
  quantile(probs = 0.875)

batch3_thres <- wpre.data  %>% 
  filter(batch == "batch_3" & 
         region == 'LW' & 
         age == 'p30') %>% 
  pull(wpre_expression) %>% 
  quantile(probs = 0.875)


batch2_thres 
   87.5% 
1.386294 
batch3_thres 
   87.5% 
1.609438 
VlnPlot(Head, "Wpre", group.by = "batch", split.by = "region") + geom_hline(yintercept = c(batch2_thres, batch3_thres, 2.1)) #There is one cell with Wpre > 2 in the Wedge region. This cell could be a contaminant or a B1 cell that transitioned to B2 between injection and cell dissociation.


VlnPlot(Head, "Wpre", group.by = "age", split.by = "region") + geom_hline(yintercept = c(batch2_thres, batch3_thres, 2.1)) #There is one cell with Wpre > 2 in the Wedge region. This cell could be a contaminant or a B1 cell that transitioned to B2 between injection and cell dissociation.

#Selecting Wpre+ cells, defined as cells with high Wpre expression, excluding cells coming from the Wedge region
wpre.data <- wpre.data %>% 
  mutate(wpre_label = case_when(age != 'p30' | batch == 'batch_1' ~ NA,
                                batch == "batch_2" & 
                                age == 'p30' &
                                wpre_expression >= batch2_thres & 
                                region!= "Wedge" & 
                                bcell_subtype != "Dorsal Pallial B cells" ~
                                  "wpre+",
                                 
                                  
                                batch == "batch_3" &
                                age == 'p30' &
                                  wpre_expression >= batch3_thres &
                                region != "Wedge" &
                                bcell_subtype != "Dorsal Pallial B cells" ~ 
                                  "wpre+",
                                .default = "wpre-"))

#Calculating the number of unlabeled B1 cells in the dataset:
wpre.data %>% 
  filter(region != "Wedge"
         ) %>% 
  group_by(batch, wpre_label) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
`summarise()` has grouped output by 'batch'. You can override using the `.groups` argument.

Identifying non-infected B1 cells

#Assuming that these cells are the most similar to Wpre+ cells.
#Splitting our dataset into different batches, selecting only cells that are not in the Wedge region.
wpre_cells_batch2 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_2") %>% 
                      rownames()

wpre_cells_batch3 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_3") %>% 
                      rownames()
batch2_lw_cells <- Head@meta.data %>% filter(region == "LW" &
                                                batch == "batch_2") %>% rownames()
batch3_lw_cells <- Head@meta.data %>% filter(region == "LW" &
                                                batch == "batch_3") %>% rownames()

Head_batch2 <- Head %>% subset(cells = batch2_lw_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:10,
                k.param = 30,
                return.neighbor = T)
Running SCTransform on assay: RNA
Running SCTransform on layer: counts.2
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 16203 by 434
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 434 cells
Found 35 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 16203 genes
Computing corrected count matrix for 16203 genes
Calculating gene attributes
Wall clock passed: Time difference of 9.660439 secs
Determine variable features
Centering data matrix

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Warning: Different cells and/or features from existing assay SCTSet default assay to SCT
PC_ 1 
Positive:  Egfr, Kcnh7, Marcks, Tmsb4x, Ptprz1, Pak3, Ccnd2, Magi1, Sall3, Fgd4 
       Fabp7, Shc3, Dscaml1, Slc4a7, Tcf12, Ppp1r14b, Map2, Hnrnpab, Hmgn2, Sox11 
       Grin2b, Slit2, H2afz, Msi2, Rpl32, Aff3, Set, Nell2, Gm29260, Nol4 
Negative:  Clu, Mt3, Gfap, Mt1, S100a6, Fxyd1, Junb, Aldoc, Fth1, Ifitm3 
       Cebpd, H2-K1, Apoe, Cst3, Ifit3, Zfp36, Serping1, B2m, Sat1, Sparc 
       Ccn1, Rarres2, Atf3, Id3, Klf2, Ifit3b, H2-D1, Dusp1, Sdc4, Prdx6 
PC_ 2 
Positive:  Nrg1, Ntrk2, Fos, 6330403K07Rik, Acsl3, Pla2g7, Grid2, Rgs6, Gpc5, Mt1 
       Gabrb1, Btg2, Lsamp, Thbs4, Rmst, Lama2, Cadm2, Dusp1, Slc15a2, Dcc 
       A330093E20Rik, Slc1a2, Htra1, Sgip1, Kcnn2, Rora, Tsc22d3, Cst3, Usp2, Lrp4 
Negative:  Bst2, B2m, Ly6e, Ifitm3, Isg15, H2-D1, Iigp1, Irf7, Ifit3, Gbp2 
       H2-K1, Rsad2, Cxcl10, Ifit3b, Psmb8, Oasl2, H2-T23, Rnf213, Igtp, Ifi44 
       Ifit1, Gm4951, Serping1, Tspo, Emp1, Psmb10, Irgm1, Vim, Psme1, Crym 
PC_ 3 
Positive:  Nrg1, Gm29260, Sparcl1, Pla2g7, Epha5, Dlgap1, Ntrk2, Rlbp1, Sema6d, Csmd1 
       Pax6, Kcnk10, Gfap, Clu, Mgat5, Hopx, Rgs6, Limch1, Ptprz1, Pls1 
       Luzp2, Lrp4, Adam23, Dgki, Mir99ahg, Add3, Mdga2, Aldoc, Arx, Astn2 
Negative:  Btg2, Junb, Dnajb1, Egr2, Hes5, Fos, Homer1, Klf2, Nr4a1, Jund 
       Ddit3, Zfp36, Maff, Egr3, Ier2, Klf4, Rhob, Ftl1, Crym, Jun 
       Notum, Actg1, Fosb, Idi1, Atf3, Egr1, Cebpb, Ubald1, Prss56, Dusp1 
PC_ 4 
Positive:  Dclk1, Nr4a3, Pde10a, Nrg1, Pla2g7, Mt1, Ntrk2, Gfap, Cebpb, Lmna 
       Clu, Gsn, Slc15a2, Ddx3y, Tuba1b, Srxn1, Emd, Mt2, Usp2, Igfbp5 
       Slc25a3, Neat1, Gm10260, Coq10b, Rorb, Emp1, Prm1, H2afz, Bag3, S100a10 
Negative:  Crym, Hes5, Hes1, Frmd4a, Dpyd, Adamts18, Grm3, Rbms3, Lrrc4c, Nrxn1 
       Prex2, Notum, Gm10561, Adgrl3, Sgcd, Egr2, Ncam1, Prss56, Pbx1, Ptprt 
       Ntm, Xist, Sntb1, Ncam2, Sema6a, Sox2ot, Negr1, Celf2, Slc1a3, Grik2 
PC_ 5 
Positive:  Ifrd1, Zswim6, Homer1, Emp1, March3, Nfatc2, Sik2, Lmna, Rorb, Creb5 
       Dclk1, Hmgcr, Cebpb, Zbtb11, Idi1, Fosb, Nebl, Ascc3, Insig1, Negr1 
       Ddit3, Msmo1, Maff, Neat1, Trib1, Dleu2, Nr4a3, Nr4a1, Atf3, Ankrd28 
Negative:  Gadd45g, Ckb, Ifi27, Itm2c, Ascl1, Gm10561, Fxn, Ppp1r14b, Tuba1b, Mt3 
       Thrsp, Apoe, Hmgn2, Hspe1, Crym, Mdk, Slc38a1, Sfrp1, Hes6, Prdx6 
       Ramp1, Ptgds, Txnip, Ubb, Nme2, Gpr37l1, Mt2, Gstm5, Cst3, Cmtm5 
Computing nearest neighbors
Only one graph name supplied, storing nearest-neighbor graph only
Head_batch3 <- Head %>% subset(cells = batch3_lw_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:10, 
                k.param = 30,
                return.neighbor = T)
Running SCTransform on assay: RNA
Running SCTransform on layer: counts.3
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 16183 by 1238
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 1238 cells
Found 45 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 16183 genes
Computing corrected count matrix for 16183 genes
Calculating gene attributes
Wall clock passed: Time difference of 15.27602 secs
Determine variable features
Centering data matrix

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Warning: Different cells and/or features from existing assay SCTSet default assay to SCT
PC_ 1 
Positive:  Cxcl10, Crym, Klf2, S100a6, Isg15, Atf3, Serpina3n, Fth1, Cebpd, H2-K1 
       Junb, Ccl2, H2-D1, Ier3, Ifit3, Ptx3, Cp, Sdc4, Ifitm3, Bst2 
       Rarres2, Vim, Mt1, Tspo, Nfkbia, Gadd45g, Mt2, Isg20, Tnfaip2, Ctsb 
Negative:  Nrg1, Nrxn1, Lsamp, Gm29260, Ptprz1, Dgkb, Mir99ahg, Zbtb20, Gpc5, Ntm 
       Pcdh9, Auts2, Msi2, Egfr, Fars2, Dcc, Ctnna2, Sparcl1, Eda, Lama2 
       Rora, Nlgn1, Negr1, Zfpm2, Celf2, Lrrc4c, Nol4, Cadm2, Pbx1, Dtna 
PC_ 2 
Positive:  Cxcl10, Rsad2, Lrrc7, Ifi27l2a, Egfr, Rnf213, Marcks, Adgrl3, Iigp1, Crym 
       Ccnd1, Emp1, Cacnb2, Ccl2, S100a10, Slc4a7, Ptx3, Sulf1, Erbb4, Ncam2 
       Ext1, Npas3, Tmsb4x, Chl1, Cadps, Gbp5, Gbp2, Atrnl1, Trib1, Arid5b 
Negative:  Aldoc, Clu, Nrg1, Mt1, Cst3, Thbs4, Cldn10, Cmss1, Ntrk2, Junb 
       Gfap, Gm42418, Apoe, Gstm5, Igfbp5, Rmst, Mt3, Acsl3, Plpp3, Pla2g7 
       Fbxo2, Dbi, Fxyd1, Btg2, Cpe, Gadd45g, Glul, Slc1a2, Dusp1, Fam107a 
PC_ 3 
Positive:  Cxcl10, Ifit2, Rsad2, Herc6, Gbp5, Gbp6, Ifit3, Slc1a2, Crym, Gbp2 
       Meg3, Nkain2, Ptprt, Slc1a3, Lsamp, Clu, Iigp1, Crot, Nrxn1, Cp 
       Igtp, Ifih1, Nlgn1, Trpm3, Gbp7, Pcdh9, Parp14, Serping1, Fndc3a, Isg15 
Negative:  Gm42418, AY036118, Cmss1, Rpl13, Rps24, Rpl32, Tmsb4x, Rpsa, Gm10260, Rplp0 
       Rpl23, Rpl35, Fabp7, Rps2, Rps8, Rpl15, Rps18, Rpl41, Rps12, Rps5 
       Rplp1, Eef1a1, Cdk8, Hist1h4d, Ppp1r14b, Hist1h1e, Marcks, Ckb, S100a10, Rpl29 
PC_ 4 
Positive:  Mt1, Sparcl1, Gm42418, Mt2, Nrg1, Pla2g7, Cxcl10, Cmss1, Aqp4, Gbp5 
       Htra1, Ccl2, AY036118, Aldoc, Clu, Ifit2, Tnfaip2, Camk1d, Gfap, Psmb10 
       Mdga2, Gm29260, Cdk8, Cp, Hopx, Peak1, Cxcl14, Kctd1, Tspo, Id2 
Negative:  Klf2, Btg2, Hes5, Dnajb1, Klf4, Ier2, Atf3, Junb, Jun, Egr1 
       Fos, Egr2, Homer1, Dusp1, Zfp36, Ddit3, Fosb, Egr3, Nfatc2, Ccn1 
       Klf6, Hes1, Rhob, Meg3, Jund, Creb5, Vcl, Mlf1, Nr4a1, Gadd45g 
PC_ 5 
Positive:  Hes5, Gm42418, Isg15, Rsad2, Iigp1, Fabp7, Cmss1, Ifit2, Pdzph1, Ifit3 
       AY036118, Cxcl10, Crym, Gm4951, Ifi27l2a, Ifit1, AW011738, Ifit3b, Ifi44, Ifih1 
       Hes1, Cmpk2, Ckb, Egfr, Parp11, Igtp, Ndufc2, Ier2, Rnf213, Cdk8 
Negative:  Ptx3, Kcnip4, Nrg1, Emp1, Trib1, Mt1, Csmd1, S100a10, Mt2, Ncam2 
       Dlg2, Vim, Gfap, Anxa2, Cadm2, Serpina3n, Hspb1, Clu, Sorcs1, Sdc4 
       Mrps6, Chl1, Gap43, Ifrd1, Dhrs1, S100a11, Msn, Pdpn, Gm4876, Atf3 
Computing nearest neighbors
Only one graph name supplied, storing nearest-neighbor graph only
#Inferring unlabeled b1 cells in batch 2:
wpre_index_batch2 <- which(Head_batch2@neighbors$SCT.nn@cell.names %in% wpre_cells_batch2)

good.neighbors_batch2 <- c()
bad.neighbors <- wpre_index_batch2 #Excluding labeled cells from the list of neighbors

for (i in wpre_index_batch2) {
  all.neighbors <- Head_batch2@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch2 <- c(good.neighbors_batch2, 
                             (all.neighbors[!all.neighbors %in% bad.neighbors])[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch2) %>% unique()
}

wpre_neighbors_batch2 <- Head_batch2@neighbors$SCT.nn@cell.names[good.neighbors_batch2]
wpre_neighbors_batch2 <- wpre_neighbors_batch2[!is.na(wpre_neighbors_batch2)]

#Inferring unlabeled b1 cells in batch 3:
wpre_index_batch3 <- which(Head_batch3@neighbors$SCT.nn@cell.names %in% wpre_cells_batch3)

good.neighbors_batch3 <- c()
bad.neighbors <- wpre_index_batch3

for (i in wpre_index_batch3) {
  all.neighbors <- Head_batch3@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch3 <- c(good.neighbors_batch3, (setdiff(all.neighbors, bad.neighbors))[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch3)
}

wpre_neighbors_batch3 <- Head_batch3@neighbors$SCT.nn@cell.names[good.neighbors_batch3]
wpre_neighbors_batch3 <- wpre_neighbors_batch3[!is.na(wpre_neighbors_batch3)]
wpre.data[, "tdtom"] <- "TdTomato-"
wpre.data[c(wpre_neighbors_batch2, wpre_neighbors_batch3),"tdtom"] <- "TdTomato+ NN"
wpre.data[c(wpre_cells_batch2, wpre_cells_batch3), "tdtom"] <- "TdTomato+"

wpre.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, tdtom) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
`summarise()` has grouped output by 'batch'. You can override using the `.groups` argument.
wpre.data %>% 
  filter(region != "Wedge" & batch == 'batch_2') %>% 
  group_by(age, tdtom) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge" & batch == 'batch_2') %>% 
              group_by(age) %>% 
              summarize(age_n = n(), by = "age")) %>%
  mutate(pct = n/age_n)
`summarise()` has grouped output by 'age'. You can override using the `.groups` argument.Joining with `by = join_by(age)`
Head$tdtom <- wpre.data$tdtom

Head@meta.data %>% 
  rownames_to_column('bc') %>% 
  left_join(wpre.data %>% rownames_to_column('bc') %>% select(bc, tdtom), by = 'bc') %>% 
column_to_rownames('bc')

Head@meta.data[, "b_type"] <- "b2"
Head@meta.data[c(wpre_cells_batch2, wpre_cells_batch3, wpre_neighbors_batch2, wpre_neighbors_batch3),"b_type"] <- "b1"

mDimPlot(Head, group.by = "b_type", shuffle = T) 

mDimPlot(Head, group.by = "b_type", split.by = "age") 

Label transfer to uninfected batch

Head@active.assay = "RNA"

non_infected_features <- rownames(Head)[!rownames(Head) %in% c('Wpre', 'GFP', 'Cre')]

Head_batch1 <- subset(Head, subset = batch == "batch_1", features = non_infected_features) %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20) %>%
  FindNeighbors(dims = 1:20)
Normalizing layer: counts.1
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.1
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  Egfr, Kcnh7, Sox11, Lrrfip1, Slc4a7, Tcf12, Tmsb4x, Map2, Setbp1, Ppp1r14b 
       Hnrnpab, Nrxn3, Sipa1l1, Shc3, Dpysl3, Ccnd2, Rpl32, Sv2c, Magi1, Dpp6 
       Chd7, Marcks, Nell2, Nfib, Itgb1, Hnrnpa1, Fgd4, Slit2, Auts2, Ybx1 
Negative:  Clu, Mt1, Mt3, Fxyd1, Mt2, S100a6, Rgcc, Junb, Ccn1, Sparc 
       Id2, Pla2g7, Id3, Cebpb, Gfap, Thbs4, Rarres2, Sat1, Gsn, Nap1l5 
       Serpine2, Zfp36, Bhlhe40, Gm39325, Gem, Dusp1, Ramp1, Dad1, Gadd45g, Slc25a3 
PC_ 2 
Positive:  Riiad1, Crym, Fam183b, Basp1, Cebpd, Dpyd, Ncam2, Ramp1, Pifo, 1700094D03Rik 
       Klf2, Cfap126, Fas, Capsl, Foxj1, Adgrl3, Cfap43, Rsph1, Ifitm3, C4b 
       Dnah9, S100a6, Slc38a1, Dnah6, Mns1, Nnat, Mlf1, C230072F16Rik, Meig1, Sorbs1 
Negative:  Nrg1, Urah, Gpc5, Cdk11b, Dclk1, Gm39325, Vps37b, Dio2, Pla2g7, Thrsp 
       Slc25a3, Epha5, Snhg15, Trpm3, Gm26512, Gsx2, Gm29260, Ppp1r3g, Ptprz1, Sgip1 
       Nkain2, Usp2, Emd, Dcc, Gm10563, Pls1, Pde10a, Arih1, Mpp7, Pmepa1 
PC_ 3 
Positive:  Stmn2, Dlx6os1, Tubb3, Gm38505, Igfbpl1, Gad2, Dcx, Nav3, Sp9, Celf4 
       Adarb2, Cacna1c, Myt1l, Sp8, Dlx5, Abracl, 2610307P16Rik, Myt1, Edil3, Anks1b 
       Shtn1, Bcl11b, Nsg2, Brinp2, Dlx1, Mir124a-1hg, Gad1, Crmp1, Elavl4, Arx 
Negative:  Egr3, Notum, Fabp7, Slit2, Egfr, Creb5, Nfatc2, Nptx2, Rfx4, Rasal2 
       Slco1c1, Ddit3, Hspa5, Hmgcs1, Adgrl3, Sik2, Aspscr1, Prss56, Crym, Slc1a3 
       Shc3, Egr2, Emp1, Pde10a, Dnajc1, Zfp521, Ascl1, Dnajb1, Ankrd28, Dusp10 
PC_ 4 
Positive:  Maff, Nr4a1, Slc25a3, Arih1, Cebpb, Nr4a3, Nop53, Vps37b, Usp2, Emd 
       Lmna, Pde10a, Btbd9, Braf, Homer1, Ifrd1, Atf3, Tpm4, Dad1, Nr4a2 
       Cdk11b, Zfp36, Anxa2, Dclk1, Srxn1, Ptma, Ppp1r15a, Isy1, Hells, Ccn1 
Negative:  Grm3, Ntm, Pcdh9, Cntnap2, Ptprt, Sox2ot, Rmst, Gm4258, B3galt1, Trim9 
       Dcc, Fjx1, Frmd4a, Veph1, Gm29260, Gm35552, Ptprz1, Hdac9, Auts2, Lrrc4c 
       Grid2, Lama2, Kctd1, 9330159F19Rik, Fabp7, Tfap2c, Pter, Zfpm2, Sall3, Cdh10 
PC_ 5 
Positive:  Arih1, Braf, Usp2, Zswim6, Meis2, Vps37b, Slco1c1, Nr4a1, Ctnna2, Btbd9 
       Maff, Pak1, Pde10a, Cdk11b, Slc1a3, Nr4a3, Homer1, Dclk1, Camk2d, Ccn1 
       Zdbf2, Tubb2a, Pcdh9, Nr4a2, Atxn7, Creb5, Meg3, Emd, Clasp2, Sik2 
Negative:  Hells, Chaf1b, Mcm6, Mcm5, Ung, Uhrf1, Pclaf, Mcm3, Cenpm, Dut 
       Mcm2, Cenph, Gins2, E2f1, Tcf19, Lig1, Gmnn, Clspn, Gadd45g, Dctpp1 
       Dhfr, Dtl, Nme2, Fignl1, Rmi2, Anp32b, Mcm7, Spc24, H2afz, Top2a 
Warning: Number of dimensions changing from 100 to 20Computing nearest neighbor graph
Computing SNN
Head_batch23 <- subset(Head, subset = batch %in% c("batch_2", "batch_3"))
Head_batch23 <- Head_batch23 %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20)
Normalizing layer: counts.2
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Normalizing layer: counts.3
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.2
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.3
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  Bst2, Tspo, Fth1, Ifit3, Psmb8, H2-K1, H2-D1, Isg15, Psmb10, Ifit3b 
       Serping1, Clu, Ifitm3, Gbp2, Cxcl10, S100a6, B2m, H2-Q4, Cebpd, Serpina3n 
       Gfap, Sdc4, H2-T23, Rarres2, Isg20, C1ra, Gm42418, Irf7, Gbp5, Ly6e 
Negative:  Auts2, Dgkb, Pbx1, Nrxn1, Sox11, Ptprz1, Kcnh7, Nol4, Lsamp, Egfr 
       Magi2, Pak3, Map2, Eda, Slit2, Zeb1, Marcks, Ccnd2, Dscaml1, Ctnna2 
       Baz2b, Grin2b, Ntm, Chd7, Setbp1, Nell2, Sall3, Cdh4, Maml3, Coro1c 
PC_ 2 
Positive:  Crym, Cxcl10, Spon1, Klf6, Atf3, Rsad2, Klf2, Egr1, Ascc3, Six3 
       Nfatc2, Tagln2, Emp1, Zfp36, Jun, Ddit3, Egr3, Gbp2, Egr2, Samd9l 
       Lmna, Crot, Ext1, Dnajb1, Gbp5, Notum, Iigp1, Trib1, Irf7, Efhb 
Negative:  Pla2g7, Mt1, Sparcl1, Thbs4, Nrg1, Fxyd6, Clu, Gm42418, Kctd1, Gfap 
       Rmst, Gm29260, Rlbp1, Gsn, Gabrb1, 9530026P05Rik, Glycam1, Epha5, Hopx, Mdga2 
       Mt2, Slc15a2, Nr2f1, Prex2, AY036118, A330093E20Rik, Pls1, Gm35552, Garnl3, Dcc 
PC_ 3 
Positive:  Gbp7, Anks1b, Rnf213, Iigp1, Znfx1, Ptprz1, Parp14, Oasl2, Gbp5, Atp10a 
       Rbms3, Ifit2, Ddx58, Trim30a, Mdga2, Gm29260, Parp11, Herc6, Slfn8, Igtp 
       Sparcl1, Ifih1, Fndc3a, Ifit3, Psmb10, Angpt1, Gbp6, Csmd2, Tor3a, Gm4951 
Negative:  Btg2, Klf4, Dusp1, Junb, Dnajb1, Zfp36, Ier2, Jun, Klf2, Mlf1 
       Maff, Nr4a1, Egr1, Egr2, Ddit3, Cebpb, Gm26887, Atf3, 6330403K07Rik, Homer1 
       Meg3, Foxj1, Ccn1, Gadd45g, Notum, Klf6, Hes1, Ubald1, Idi1, Insig1 
PC_ 4 
Positive:  Tmsb4x, Ppp1r14b, AY036118, Tmsb10, H2afz, Gm42418, Tubb5, Hmgn2, Lrrc7, Selenoh 
       Lrrfip1, S100a10, Top2a, Stmn1, Hells, Cdca8, Cks2, Slit1, Uhrf1, Mki67 
       Cenpk, Mcm2, Ccnd1, Gm38505, Dcx, Nsg2, Gm10260, Mcm5, Cenpf, Dpysl3 
Negative:  Lsamp, Nrxn1, Nkain2, Cadm2, Csmd1, Gabrb1, Pcdh9, Nlgn1, Grid2, Prex2 
       Rgs6, Ppp2r2b, Nrg1, Herc6, Dlgap1, Luzp2, Dgkb, Slc35f1, Vcl, Bmpr1b 
       Dcc, Nckap5, Timp3, Hdac8, Auts2, Klf4, Ntm, Lrrc4c, Plagl1, Fgf13 
PC_ 5 
Positive:  Fabp7, Egfr, Lrrfip1, Ptprz1, Olig2, Nptx2, Hes5, Sv2c, Marcks, Sall3 
       Thrsp, Ascl1, Slc4a7, Ccnd1, Rhcg, Shc3, Olig1, Adam12, Sema5b, Cdk6 
       Dll1, Crlf1, Me3, A930037H05Rik, Ifi27l2a, Nes, Pvt1, Hs2st1, Rnf213, Coro1c 
Negative:  Dlx6os1, Dcx, Nav3, Gm38505, Stmn2, Tubb3, Cacna1c, Sp9, Clu, Shtn1 
       Gad2, Adarb2, Myt1l, Celf4, Igfbpl1, Islr2, Gad1, Pak7, Synpr, Kcnh8 
       Mt1, Tenm2, Pcdha-all, Dlx5, Bcl11b, Plxna4, Enox1, Basp1, Nrxn3, Stmn4 
Warning: Number of dimensions changing from 100 to 20
Head_batch23 <- IntegrateLayers(Head_batch23, method = CCAIntegration, orig.reduction = "pca", dims = 1:20)
Finding all pairwise anchors

  |                                                  | 0 % ~calculating  
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 2139 anchors

  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: Number of dimensions changing from 100 to 20
Head.anchors <- Head_batch23 %>%
  FindTransferAnchors(reference = .,
                      query = Head_batch1,
                      dims = 1:20,
                      reference.reduction = "integrated.dr")
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 1308 anchors
predictions <- TransferData(anchorset = Head.anchors, refdata = Head_batch23$b_type, dims = 1:20)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
inferredb1_batch1_cells <- predictions %>% filter(predicted.id != "b2") %>% rownames()
Head@meta.data[inferredb1_batch1_cells, "tdtom"] <- "TdTomato+ LT"
Head@meta.data[inferredb1_batch1_cells, "b_type"] <- "b1"

#---------------------------------

# Head@active.assay = "RNA"
# non_infected_features <- rownames(Head)[!rownames(Head) %in% c('Wpre', 'GFP', 'Cre')]
# 
# Head_batch1 <- Head %>% 
#   subset(subset = batch == "batch_1", features = non_infected_features) %>% 
#   NormalizeData() %>%
#   FindVariableFeatures() %>%
#   ScaleData() %>% 
#   RunPCA(assay = "RNA", 
#          npcs = 20) %>% 
#   FindNeighbors(dims = 1:20)
# 
# 
# Head_batch23 <- Head %>% subset(subset = batch != "batch_1", features = non_infected_features)
# Head_batch23 <- JoinLayers(Head_batch23, assay = "RNA")
# Head_batch23[["RNA"]] <- split(Head_batch23[["RNA"]], f = Head_batch23$batch)
# 
# Head_batch23 <- Head_batch23 %>% 
#   NormalizeData() %>%
#   FindVariableFeatures() %>%
#   ScaleData() %>% 
#   RunPCA(assay = "RNA", 
#          npcs = 20)
# Head_batch23 <- IntegrateLayers(Head_batch23, method = CCAIntegration, orig.reduction = "pca", dims = 1:20)
# 
# Head.anchors <- Head_batch23 %>% 
#   FindTransferAnchors(reference = ., 
#                       query = Head_batch1, 
#                       dims = 1:20,
#                       reference.reduction = "pca")
# 
# predictions <- TransferData(anchorset = Head.anchors, 
#                             refdata = Head_batch23$b_type, 
#                             dims = 1:20)
# 
# predictions %>% filter(predicted.id =='b1')
# 
# inferredb1_batch1_cells <- predictions %>% filter(predicted.id == "b1") %>% rownames()
# Head@meta.data[inferredb1_batch1_cells, "tdtom"] <- "TdTomato+ LT"
# Head@meta.data[inferredb1_batch1_cells, "b_type"] <- "b1"
Head@active.assay <- 'SCT'

mFeaturePlot(Head, features = c("S100a6", "Egfr", "Crym", "Urah", "Tfap2c"), order = T, ncol = 5, legend = F) 
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

mDimPlot(Head, group.by = "age",  order = T) + 
  scale_color_manual(values = c('grey90', 'tomato'))


mDimPlot(Head, group.by = "activation_state", split.by = "age", shuffle = T) + 
  scale_color_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') 


mFeaturePlot(Head, features = c("Crym", "Egfr"), split.by = "age",  order = T) 
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

mDimPlot(Head, group.by = "b_type",  shuffle = T) + 
  scale_color_manual(values = c("b1" = "skyblue1", "b2" = "mediumblue"), labels = c('B1', 'B2'))


mDimPlot(Head, group.by = "b_type", split.by = "age", shuffle = T) + 
  scale_color_manual(values = c("b1" = "skyblue1", "b2" = "mediumblue"), labels = c('B1', 'B2'))


mDimPlot(Head, group.by = "tdtom", order = T) + 
  scale_color_manual(values = c("TdTomato+" = "magenta", 
                                "TdTomato+ NN" = "darkorange", 
                                "TdTomato+ LT" = 'darkgreen'),
                     na.value = 'grey90')


mDimPlot(Head, group.by = "tdtom", split.by = 'tdtom', order = T) + 
  scale_color_manual(values = c("TdTomato+" = "magenta", 
                                "TdTomato+ NN" = "darkorange", 
                                "TdTomato+ LT" = 'darkgreen'),
                     na.value = 'grey90')

mDimPlot(Head, group.by = "SCT_snn_res.1",  shuffle = T, label = T, legend = F)

mDimPlot(Head, group.by = "SCT_snn_res.1",  split.by = 'region', shuffle = T, label = T, legend = F)

Head@meta.data %>% 
  ggplot(aes(x = age, fill = b_type)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_fill_manual(values = c("b1" = "skyblue1", "b2" = "mediumblue"), labels = c('B1', 'B2')) +
  scale_y_continuous(labels = scales::percent, expand = c(0,0)) +
  labs(x = "Age", y = "Fraction of cells", fill = "B cell type") 
ggsave('../figures/b1b2_barplot.pdf', width = 5, height = 3)

-Activation/Quiescence

in B1 and B2 cells

a <- Head@meta.data %>% 
  filter(region == 'LW' & age == 'p30') %>% 
  ggplot(aes(x = b_type, fill = activation_state)) +
  geom_bar() +
  theme_classic() + 
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_discrete(labels = c('B1', 'B2')) + 
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') +
  labs(x = NULL, y = 'Count')

b <- Head@meta.data %>% 
  filter(region == 'LW' & age == 'p30') %>% 
  ggplot(aes(x = b_type, fill = activation_state)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
  scale_x_discrete(labels = c('B1', 'B2')) +  
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') +
  labs(x = NULL, y = 'Share')

a + b + plot_layout(guides = 'collect') + plot_annotation(caption = 'Only cells in the LW region at p30 are shown.')

in p30 and p365

c <- Head@meta.data %>% 
  filter(region == 'LW') %>% 
  ggplot(aes(x = age, fill = activation_state)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') +
  labs(x = NULL, y = 'Share')

b + c + plot_layout(guides = 'collect', axis_titles = 'collect_y') + plot_annotation(caption = 'Only cells in the LW region are shown.')
ggsave("../figures/activation_states.pdf", width = 5, height = 3)

#- DEGs

Idents(Head) <- "b_type"

HeadQ <- Head %>% subset(subset = activation_state == 'quiescent') %>% 
  SCTransform(return.only.var.genes = F) %>% 
  PrepSCTFindMarkers()
Running SCTransform on assay: RNA
Running SCTransform on layer: counts.1
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 17603 by 2321
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 2321 cells
Found 100 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 17603 genes
Computing corrected count matrix for 17603 genes
Calculating gene attributes
Wall clock passed: Time difference of 26.05989 secs
Determine variable features
Centering data matrix

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Running SCTransform on layer: counts.2
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 14890 by 323
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 323 cells
Found 42 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 14890 genes
Computing corrected count matrix for 14890 genes
Calculating gene attributes
Wall clock passed: Time difference of 6.948273 secs
Determine variable features
Centering data matrix

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Running SCTransform on layer: counts.3
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 16617 by 1889
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 1889 cells
Found 48 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 16617 genes
Computing corrected count matrix for 16617 genes
Calculating gene attributes
Wall clock passed: Time difference of 16.08269 secs
Determine variable features
Centering data matrix

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Centering data matrix

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Centering data matrix

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Warning: Different cells and/or features from existing assay SCTSet default assay to SCT
Found 3 SCT models. Recorrecting SCT counts using minimum median counts: 6415

  |                                                  | 0 % ~calculating  
  |+++++++++++++++++                                 | 33% ~38s          
  |++++++++++++++++++++++++++++++++++                | 67% ~12s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=40s  
btype_markers <- FindAllMarkers(HeadQ, 
                                logfc.threshold = 0, 
                                min.pct = 0, 
                                only.pos = T)
Calculating cluster b1
Calculating cluster b2
btype_markers <- btype_markers %>%
  group_by(cluster) %>%
  mutate(rank = rank(p_val_adj, ties.method = "first")) %>%
  arrange(cluster, rank)

#- Volcano Plot

validated.genes = c("Atf3", "Ptprz1", "Riiad1", "FoxJ1", "Gadd45b", "Zeb1", "Tagln2", "Emp1", "Anxa2")
regional.genes = c("Crym", "Nrg1", "Klf2", "Cebpd", "Gm29260", "Pax6", "Rlbp1", "Nkx6-2")
b1_degenes = btype_markers %>% filter(cluster == "b1") %>% rownames()
b2_degenes = btype_markers %>% filter(cluster == "b2") %>% rownames()
genes_to_highlight = c(b1_degenes[1:5], b2_degenes[1:5],validated.genes, regional.genes)

volcano.data = btype_markers %>% 
                mutate(labs = if_else(gene %in% genes_to_highlight, gene, "")) %>% 
                mutate(significant = case_when(p_val_adj < 0.05 & 
                                               cluster == "b1" ~ "b1",
                                               p_val_adj < 0.05 & cluster == "b2" ~ "b2",
                                               TRUE ~ "Not differentially expressed")) %>% 
                mutate(significant = if_else(gene%in%regional.genes, "regional.genes", significant)) %>%
                mutate(log2FC = case_when(cluster == "b2" ~ avg_log2FC*-1,
                                          TRUE ~ avg_log2FC)) %>% 
                mutate(significant = if_else(gene%in%validated.genes, "Validated gene", significant)) %>%
                mutate(priority = gene %in% genes_to_highlight) %>%
                arrange(priority, gene) %>%
                select(-priority) 
                
  volcano.data %>% 
  ggplot(aes(log2FC, -log(base = 10, p_val_adj),  label = labs)) + 
  geom_point(aes(col = significant), stroke = 0, size = 4, alpha = 0.5) + 
  scale_color_manual(name = NULL, labels = c("Upregulated",
                                             "Downregulated",
                                             "Not differentially expressed",
                                             "Regionalization-associated genes",
                                             "Validated gene"
                                             ), values = c("skyblue1", "mediumblue", "grey75","darkgreen","magenta")) +
  geom_text_repel(max.overlaps = Inf, size=5, box.padding=0.3) +
  theme_classic() +
  theme(panel.grid.minor = element_blank(), 
        text = element_text(family = "Helvetica", size = 15)
        ) + 
  labs(title = "Differential expression between B1 and B2 cells",
       x = "Average log2FC",
       y = "-log10 adjusted P value")
  
ggsave("data/btype_volcano.pdf", width = 6, height = 4)

#-Cell Cycle Score

g2m.genes
 [1] "Hmgb2"   "Cdk1"    "Nusap1"  "Ube2c"   "Birc5"   "Tpx2"    "Top2a"   "Ndc80"   "Cks2"   
[10] "Nuf2"    "Cks1b"   "Mki67"   "Tmpo"    "Cenpf"   "Tacc3"   "Fam64a"  "Smc4"    "Ccnb2"  
[19] "Ckap2l"  "Ckap2"   "Aurkb"   "Bub1"    "Kif11"   "Anp32e"  "Tubb4b"  "Gtse1"   "Kif20b" 
[28] "Hjurp"   "Cdca3"   "Hn1"     "Cdc20"   "Ttk"     "Cdc25c"  "Kif2c"   "Rangap1" "Ncapd2" 
[37] "Dlgap5"  "Cdca2"   "Cdca8"   "Ect2"    "Kif23"   "Hmmr"    "Aurka"   "Psrc1"   "Anln"   
[46] "Lbr"     "Ckap5"   "Cenpe"   "Ctcf"    "Nek2"    "G2e3"    "Gas2l3"  "Cbx5"    "Cenpa"  
ggsave("..figures/activation_states.pdf", width = 5, height = 3)
Cannot find directory ]8;;file:///media/data5/marcos/b2/manuscript/2.analysis/..figures..figures]8;;.
ℹ Would you like to create a new directory?

1: Yes
2: No
2
Error in `ggsave()`:
! Cannot find directory ]8;;file:///media/data5/marcos/b2/manuscript/2.analysis/..figures..figures]8;;.
ℹ Please supply an existing directory or use `create.dir = TRUE`.
Backtrace:
 1. ggplot2::ggsave("..figures/activation_states.pdf", width = 5, height = 3)

-B1 score

Head@active.assay <- "SCT"
b.markers.list = list(b1 = c("Atf3", "Riiad1", "Foxj1", "Gadd45b", "Tagln2", "Emp1"))

Head = AddModuleScore(Head, features = b.markers.list)
Head@meta.data$B1_score <- NULL 
Head@meta.data = Head@meta.data %>% dplyr::rename(B1_score = Cluster1)

#Re-scaling Module Scores:These lines rescale the module scores (B1_score and B2_score) so that they have a mean of 0 and a standard deviation of 1. This step standardizes the scores, making them comparable across different modules.

Head$B1_score = Head$B1_score %>% rescale() 

mFeaturePlot(Head, features = "B1_score", order = T) & 
  scale_color_distiller(type = 'seq', palette = 16, direction = 1)
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

saveRDS(Head, "data/Head_withp365.rds")
mFeaturePlot(Head, features = c('Atf3', 'Tagln2', 'Emp1'), order = T, ncol = 3, legend = F)
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

mFeaturePlot(HeadQ, 
             features = b.markers.list$b1, 
             split.by = 'b_type', 
             order = T,
             legend = F)
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

DotPlot(HeadQ, b.markers.list$b1, group.by = 'b_type', scale = F )

Head@meta.data %>% filter(activation_state == 'quiescent') %>% 
  ggplot(aes(x = age, y = B1_score, color = b_type)) +
  geom_jitter(height = 0, alpha = 0.2, size = 0.5) +
  geom_boxplot(outliers = F, fill = NA, size = 0.8) +
  theme_classic() +
  scale_y_continuous() +
  scale_color_manual(name = 'B Type', 
                     values = c('b1' = 'skyblue', 'b2' = 'mediumblue'), 
                     labels = c('B1', 'B2')) + 
  labs(title = 'B1 Score in quiescent B cells', x = 'Age', y = 'B1 Score') +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5))

ggsave('../figures/b1_scores_by_age.pdf')
Saving 7.29 x 4.51 in image

#-Heatmap

Colors

#Main color
col_values <- seq(-2, 2, 0.5)
b1_score_values <- seq(0, 1, 0.125)
col_fun <- colorRamp2(c(-2, 0, 2), c("cyan", "white", "magenta"))

#annotations colors
top_anno_colors <- list(Dissection = c("LV" = "grey90", "LW" = "purple", "Wedge" = "forestgreen"),
                         `B Cell type` = c("b1" = 'skyblue1', "b2" = "mediumblue"),
                        `B1 Score` = colorRamp2(b1_score_values, brewer.pal(n = length(b1_score_values), name = "YlGnBu")))

Data

pdf("../figures/b1b2_degenes_heatmap.pdf", width = 8.2, height = 5.8)
hm
dev.off()
null device 
          1 

#P9+P30+P365 ——————————-

# Head <- subset(integrated_exp, cells = b_cells)
Head <- Head %>% 
        SCTransform() %>% 
        RunPCA(assay = "SCT", npcs = 100) %>% 
        IntegrateLayers(method               = CCAIntegration, 
                        normalization.method = "SCT", 
                        verbose              = F) %>% 
        RunUMAP(dims = 1:100, reduction = "integrated.dr") %>% 
        FindNeighbors(reduction = "integrated.dr", dims = 1:100) %>% 
        FindClusters(resolution = seq(0.5, 2, 0.5), graph.name = "SCT_snn")

DimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1") + 
  NoLegend()  + 
  coord_fixed() + 
  NoAxes()
---
title: "B2 cells Analysis"
output: html_notebook
author: "Marcos Nascimento and Arantxa Cebrian-Silla" 
---

#Setup
```{r setup}
library(Seurat)
library(tidyverse)
library(scales)
library(viridis)
library(patchwork)
library(ggrepel)



#set working directory to the location of the file
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

source("../my_functions.R") #Loading a couple of custom functions for plotting
```

# P30 Only -------------------------------
```{r}
b_cells <- read.table('b_cells.txt') %>% pull(V1)
```

#-Subset B cell clusters
```{r}
#Idents(Neurogenic_lineage) = "SCT_snn_res.2"
Head <- subset(integrated_exp, cells = b_cells)
```

```{r}
Head <- Head %>% 
        SCTransform() %>% 
        RunPCA(assay = "SCT", npcs = 100) %>% 
        IntegrateLayers(method               = CCAIntegration, 
                        normalization.method = "SCT", 
                        verbose              = F) %>% 
        RunUMAP(dims = 1:100, reduction = "integrated.dr") %>% 
        FindNeighbors(reduction = "integrated.dr", dims = 1:100) %>% 
        FindClusters(resolution = seq(0.5, 2, 0.5), graph.name = "SCT_snn")

DimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1") + 
  NoLegend()  + 
  coord_fixed() + 
  NoAxes()
```

## Rotating the UMAP coordinates 
```{r}
umap_original <- Head@reductions$umap@cell.embeddings

# Custom function to create a 2D rotation matrix
rot2 <- function(angle_radians) {
  matrix(c(cos(angle_radians), -sin(angle_radians),
           sin(angle_radians), cos(angle_radians)), nrow = 2)
}

# Function to rotate multiple points using a matrix of coordinates
rotate_points <- function(points, angle_degrees) {
  # Convert the angle from degrees to radians
  angle_radians <- angle_degrees * pi / 180
  
  # Get the rotation matrix
  rotation_matrix <- rot2(angle_radians)
  
  # Apply the rotation matrix to the matrix of points (transpose for correct multiplication)
  rotated_points <- t(rotation_matrix %*% t(points))
  
  return(rotated_points)
}


# Rotate all points by 45 degrees
umap_rotated <- rotate_points(umap_original, 90)
colnames(umap_rotated) <- c("umap_1", "umap_2")
Head@reductions$umap@cell.embeddings <- umap_rotated
``` 

```{r}
DimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1") + 
  NoLegend()  + 
  coord_fixed() + 
  NoAxes()
```
# Nearest Neightbors Analysis to Identify B1 cells
```{r}
DimPlot(Head, group.by = c("cell_type_label", "region")) & NoAxes() & coord_fixed()

FeaturePlot(Head, "Wpre", order = T) + scale_color_viridis(option = "A")
```


####-Identifying  Tdtomato+ (Wpre+) cells 
```{r}
VlnPlot(Head, "Wpre", group.by = "batch", split.by = "region") #There is one cell with Wpre > 2 in the Wedge region. This cell could be a contaminant or a B1 cell that transitioned to B2 between injection and cell dissociation.
``` 


```{r}
#Assuming that B1 and B2 cells co-exist in similar numbers at P30, and only 25% of B1 cells are infected, we should select the top 12.5% Wpre-expressing cells in each batch.
wpre.data = Head@meta.data %>% 
  mutate(wpre_expression = Head@assays$SCT@data["Wpre",] %>% 
         as.data.frame() %>% 
         pull("."))

batch2_thres <- wpre.data %>% filter(batch == "batch_2") %>% pull(wpre_expression) %>% quantile(probs = 0.875)
batch3_thres <- wpre.data %>% filter(batch == "batch_3") %>% pull(wpre_expression) %>% quantile(probs = 0.875)

batch2_thres
batch3_thres
```


```{r}
#Selecting Wpre+ cells, defined as cells with normalized Wpre expression > 1.39, excluding cells coming from the Wedge region.
wpre.data <- wpre.data %>% 
  mutate(wpre_label = case_when(batch == "batch_2" & 
                                  wpre_expression > batch2_thres & 
                                  region!= "Wedge" ~ "wpre+",
                                batch == "batch_3" &
                                  wpre_expression > batch3_thres &
                                region != "Wedge" ~ "wpre+",
                                .default = "wpre-"))

#Calculating the number of unlabeled B1 cells in the dataset:
wpre.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, wpre_label) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
```

####-Identifying non-infected B1 cells
```{r}
#Assuming that these cells are the most similar to Wpre+ cells.
#Splitting our dataset into different batches, selecting only cells that are not in the Wedge region.
wpre_cells_batch2 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_2") %>% 
                      rownames()

wpre_cells_batch3 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_3") %>% 
                      rownames()
```

```{r}
nonwedge_batch2_cells <- Head@meta.data %>% filter(region != "Wedge" & batch == "batch_2") %>% rownames()
nonwedge_batch3_cells <- Head@meta.data %>% filter(region != "Wedge" & batch == "batch_3") %>% rownames()

Head_batch2 <- Head %>% subset(cells = nonwedge_batch2_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:100, 
                k.param = 30,
                return.neighbor = T)

Head_batch3 <- Head %>% subset(cells = nonwedge_batch3_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:100, 
                k.param = 30, 
                return.neighbor = T)
```

```{r}
#Inferring unlabeled b1 cells in batch 2:
wpre_index_batch2 <- which(Head_batch2@neighbors$SCT.nn@cell.names %in% wpre_cells_batch2)

good.neighbors_batch2 <- c()
bad.neighbors <- wpre_index_batch2

for (i in wpre_index_batch2) {
  all.neighbors <- Head_batch2@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch2 <- c(good.neighbors_batch2, 
                             (all.neighbors[!all.neighbors %in% bad.neighbors])[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch2) %>% unique()
}

wpre_neighbors_batch2 <- Head_batch2@neighbors$SCT.nn@cell.names[good.neighbors_batch2]
wpre_neighbors_batch2 <- wpre_neighbors_batch2[!is.na(wpre_neighbors_batch2)]

#Inferring unlabeled b1 cells in batch 3:
wpre_index_batch3 <- which(Head_batch3@neighbors$SCT.nn@cell.names %in% wpre_cells_batch3)

good.neighbors_batch3 <- c()
bad.neighbors <- wpre_index_batch3

for (i in wpre_index_batch3) {
  all.neighbors <- Head_batch3@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch3 <- c(good.neighbors_batch3, (setdiff(all.neighbors, bad.neighbors))[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch3)
}

wpre_neighbors_batch3 <- Head_batch3@neighbors$SCT.nn@cell.names[good.neighbors_batch3]
wpre_neighbors_batch3 <- wpre_neighbors_batch3[!is.na(wpre_neighbors_batch3)]
```

```{r}
wpre.data[, "b_type"] <- "b2"
wpre.data[c(wpre_neighbors_batch2, wpre_neighbors_batch3),"b_type"] <- "b1 - nn"
wpre.data[c(wpre_cells_batch2, wpre_cells_batch3), "b_type"] <- "b1 - labeled"

wpre.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, b_type) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)

```

```{r}
Head@meta.data[, "b_type"] <- "b2"
Head@meta.data[c(wpre_neighbors_batch2, wpre_neighbors_batch3),"b_type"] <- "b1 - nn"
Head@meta.data[c(wpre_cells_batch2, wpre_cells_batch3), "b_type"] <- "b1 - labeled"

DimPlot(Head, group.by = "b_type", shuffle = T) & NoAxes() & coord_fixed()
DimPlot(Head, group.by = "b_type", split.by = "b_type") & NoAxes() & coord_fixed()
```

####-Label transfer to uninfected cells
```{r}
Head@active.assay = "RNA"

non_infected_features <- rownames(Head)[!rownames(Head) %in% c('Wpre', 'GFP', 'Cre')]

Head_batch1 <- subset(Head, subset = batch == "batch_1", features = non_infected_features) %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20) %>%
  FindNeighbors(dims = 1:20)


Head_batch23 <- subset(Head, subset = batch %in% c("batch_2", "batch_3"))
Head_batch23 <- Head_batch23 %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20)
Head_batch23 <- IntegrateLayers(Head_batch23, method = CCAIntegration, orig.reduction = "pca", dims = 1:20)

Head.anchors <- Head_batch23 %>%
  FindTransferAnchors(reference = .,
                      query = Head_batch1,
                      dims = 1:20,
                      reference.reduction = "pca")

predictions <- TransferData(anchorset = Head.anchors, refdata = Head_batch23$b_type, dims = 1:20)

inferredb1_batch1_cells <- predictions %>% filter(predicted.id != "b2") %>% rownames()
Head@meta.data[inferredb1_batch1_cells, "b_type"] <- "b1 - nn"
```

```{r}
Head@meta.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, b_type) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
```

```{r}
FeaturePlot(Head, c("S100a6", "Egfr", "Crym", "Urah", "Tfap2c"), order = T, ncol = 5) & 
  scale_color_viridis(option = "A") & 
  coord_fixed() & simple


Idents(Head) = "SCT_snn_res.1"

Head@meta.data <- Head@meta.data %>%
  mutate(cell_subtype = case_when(
    SCT_snn_res.1 %in% c(4, 2, 8, 11, 10) ~ "aB cells",
    SCT_snn_res.1 == 0 ~ "Ventral Subpallial B cells",
    SCT_snn_res.1 %in% c(3, 5) ~ "Dorsal Pallial B cells",
    SCT_snn_res.1 %in% c(6, 1, 9, 7) ~ "Dorsal Subpallial B cells",
    TRUE ~ "Unknown"
  ))

Head@meta.data <- Head@meta.data %>%
  mutate(activation_state = case_when(
    cell_subtype == "aB cells" ~ "activated",
    TRUE ~ "quiescent"
  ))

Idents(Head) = "SCT_snn_res.1"
DimPlot(Head, 
        group.by = "cell_subtype", 
        label=F, 
        cols = c("cyan","blue","dodgerblue2","dodgerblue4", "dodgerblue3")) +
  NoLegend() + 
  coord_fixed() + 
  NoAxes()

DimPlot(Head, group.by ="region", label = F, shuffle = F, cols = c("grey", "purple", "dodgerblue2"), pt.size = 1.5) & NoAxes() & coord_fixed()

#B1 score -----------------------------
Head@active.assay <- "SCT"
b.markers.list = list(b1 = c("Atf3", "Riiad1", "Foxj1", "Gadd45b", "Tagln2", "Emp1"))

Head = AddModuleScore(Head, features = b.markers.list)
Head@meta.data  = Head@meta.data %>% dplyr::rename(B1_score = Cluster1)

#Re-scaling Module Scores:These lines rescale the module scores (B1_score and B2_score) so that they have a mean of 0 and a standard deviation of 1. This step standardizes the scores, making them comparable across different modules.

Head$B1_score = Head$B1_score %>% rescale() 

FeaturePlot(Head, c("B1_score"), order = T, pt.size = 1.5) & 
  scale_color_viridis(option = "magma") & 
  NoAxes() & 
  coord_fixed()

saveRDS(Head, "data/Head.rds")
```



# P30+P365  -------------------------------

```{r}
integrated_exp <- readRDS("../pre-processing/integrated_exp_withp365.rds")
```

```{r}
mDimPlot(integrated_exp, group.by = "cell_type", label = T, shuffle = T, legend = F, repel = T)
```

# -Subset B clusters
```{r}
b_cells <- integrated_exp@meta.data %>% filter(cell_type == 'B cells') %>% rownames()
```

```{r}
Head <- subset(integrated_exp, cells = b_cells)
```

```{r}
Head <- Head %>% 
        SCTransform() %>% 
        RunPCA(assay = "SCT", npcs = 100) %>% 
        IntegrateLayers(method               = CCAIntegration, 
                        normalization.method = "SCT", 
                        verbose              = F) %>% 
        RunUMAP(dims = 1:100, reduction = "integrated.dr") %>% 
        FindNeighbors(reduction = "integrated.dr", dims = 1:100) %>% 
        FindClusters(resolution = seq(0.5, 2, 0.5), graph.name = "SCT_snn")

mDimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1", legend = F)
```
## Rotating the UMAP coordinates 
```{r}
umap_original <- Head@reductions$umap@cell.embeddings

# Rotate all points by 45 degrees
umap_rotated <- rotate_points(umap_original, 180)
colnames(umap_rotated) <- c("umap_1", "umap_2")
Head@reductions$umap@cell.embeddings <- umap_rotated

mDimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.0.5", legend = F)
``` 

## Assigning regions
```{r}
Head@meta.data <- Head@meta.data %>%
  mutate(bcell_subtype = case_when(
    SCT_snn_res.1 %in% c(10, 1, 7) ~ "aB cells",
    SCT_snn_res.1 %in% c(3, 5, 8) ~ "Ventral Subpallial B cells",
    SCT_snn_res.1 %in% c(0, 6) ~ "Dorsal Pallial B cells",
    SCT_snn_res.1 %in% c(4, 2, 9) ~ "Dorsal Subpallial B cells",
    TRUE ~ "Unknown"
  ))

Head@meta.data <- Head@meta.data %>%
  mutate(activation_state = case_when(
    bcell_subtype == "aB cells" ~ "activated",
    TRUE ~ "quiescent"
  ))

mDimPlot(Head, 
        group.by = "bcell_subtype", 
        label = F, 
        cols = c("cyan","blue","dodgerblue2","dodgerblue4", "dodgerblue3")) 

mDimPlot(Head, group.by = "region", label = F, shuffle = F, cols = c("grey80", "purple", "forestgreen")) 
``` 

#- B1 / B2 identification
### Identifying  Tdtomato+ (Wpre+) cells 

```{r}
#Assuming that B1 and B2 cells co-exist in similar numbers at P30, and only 25% of B1 cells are infected, we should select the top 12.5% Wpre-expressing cells in each batch.
wpre.data = Head@meta.data %>% 
  mutate(wpre_expression = Head@assays$SCT@data["Wpre",] %>% 
         as.data.frame() %>% 
         pull("."))

batch2_thres <- wpre.data %>% 
  filter(batch == "batch_2" & 
         region == 'LW' & 
         age == 'p30') %>% 
  pull(wpre_expression) %>% 
  quantile(probs = 0.875)

batch3_thres <- wpre.data  %>% 
  filter(batch == "batch_3" & 
         region == 'LW' & 
         age == 'p30') %>% 
  pull(wpre_expression) %>% 
  quantile(probs = 0.875)


batch2_thres 
batch3_thres 
```

```{r}
VlnPlot(Head, "Wpre", group.by = "batch", split.by = "region") + geom_hline(yintercept = c(batch2_thres, batch3_thres, 2.1)) #There is one cell with Wpre > 2 in the Wedge region. This cell could be a contaminant or a B1 cell that transitioned to B2 between injection and cell dissociation.

VlnPlot(Head, "Wpre", group.by = "age", split.by = "region") + geom_hline(yintercept = c(batch2_thres, batch3_thres, 2.1)) #There is one cell with Wpre > 2 in the Wedge region. This cell could be a contaminant or a B1 cell that transitioned to B2 between injection and cell dissociation.
``` 


```{r}
#Selecting Wpre+ cells, defined as cells with high Wpre expression, excluding cells coming from the Wedge region
wpre.data <- wpre.data %>% 
  mutate(wpre_label = case_when(age != 'p30' | batch == 'batch_1' ~ NA,
                                batch == "batch_2" & 
                                age == 'p30' &
                                wpre_expression >= batch2_thres & 
                                region!= "Wedge" & 
                                bcell_subtype != "Dorsal Pallial B cells" ~
                                  "wpre+",
                                 
                                  
                                batch == "batch_3" &
                                age == 'p30' &
                                  wpre_expression >= batch3_thres &
                                region != "Wedge" &
                                bcell_subtype != "Dorsal Pallial B cells" ~ 
                                  "wpre+",
                                .default = "wpre-"))

#Calculating the number of unlabeled B1 cells in the dataset:
wpre.data %>% 
  filter(region != "Wedge"
         ) %>% 
  group_by(batch, wpre_label) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)
```
### Identifying non-infected B1 cells
```{r}
#Assuming that these cells are the most similar to Wpre+ cells.
#Splitting our dataset into different batches, selecting only cells that are not in the Wedge region.
wpre_cells_batch2 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_2") %>% 
                      rownames()

wpre_cells_batch3 <- wpre.data %>% 
                      filter(wpre_label == "wpre+" &
                             batch == "batch_3") %>% 
                      rownames()
```

```{r}
batch2_lw_cells <- Head@meta.data %>% filter(region == "LW" &
                                                batch == "batch_2") %>% rownames()
batch3_lw_cells <- Head@meta.data %>% filter(region == "LW" &
                                                batch == "batch_3") %>% rownames()

Head_batch2 <- Head %>% subset(cells = batch2_lw_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:10,
                k.param = 30,
                return.neighbor = T)

Head_batch3 <- Head %>% subset(cells = batch3_lw_cells) %>%
  SCTransform() %>% 
  RunPCA(assay = "SCT", 
         npcs = 100) %>% 
  FindNeighbors(dims = 1:10, 
                k.param = 30,
                return.neighbor = T)
```

```{r}
#Inferring unlabeled b1 cells in batch 2:
wpre_index_batch2 <- which(Head_batch2@neighbors$SCT.nn@cell.names %in% wpre_cells_batch2)

good.neighbors_batch2 <- c()
bad.neighbors <- wpre_index_batch2 #Excluding labeled cells from the list of neighbors

for (i in wpre_index_batch2) {
  all.neighbors <- Head_batch2@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch2 <- c(good.neighbors_batch2, 
                             (all.neighbors[!all.neighbors %in% bad.neighbors])[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch2) %>% unique()
}

wpre_neighbors_batch2 <- Head_batch2@neighbors$SCT.nn@cell.names[good.neighbors_batch2]
wpre_neighbors_batch2 <- wpre_neighbors_batch2[!is.na(wpre_neighbors_batch2)]

#Inferring unlabeled b1 cells in batch 3:
wpre_index_batch3 <- which(Head_batch3@neighbors$SCT.nn@cell.names %in% wpre_cells_batch3)

good.neighbors_batch3 <- c()
bad.neighbors <- wpre_index_batch3

for (i in wpre_index_batch3) {
  all.neighbors <- Head_batch3@neighbors$SCT.nn@nn.idx[i,2:30]
  good.neighbors_batch3 <- c(good.neighbors_batch3, (setdiff(all.neighbors, bad.neighbors))[1:3])
  bad.neighbors <- c(bad.neighbors, good.neighbors_batch3)
}

wpre_neighbors_batch3 <- Head_batch3@neighbors$SCT.nn@cell.names[good.neighbors_batch3]
wpre_neighbors_batch3 <- wpre_neighbors_batch3[!is.na(wpre_neighbors_batch3)]
```

```{r}
wpre.data[, "tdtom"] <- "TdTomato-"
wpre.data[c(wpre_neighbors_batch2, wpre_neighbors_batch3),"tdtom"] <- "TdTomato+ NN"
wpre.data[c(wpre_cells_batch2, wpre_cells_batch3), "tdtom"] <- "TdTomato+"

wpre.data %>% 
  filter(region != "Wedge") %>% 
  group_by(batch, tdtom) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge") %>% 
              group_by(batch) %>% 
              summarize(batch_n = n()), by = "batch") %>%
  mutate(pct = n/batch_n)

```

```{r}
wpre.data %>% 
  filter(region != "Wedge" & batch == 'batch_2') %>% 
  group_by(age, tdtom) %>% 
  summarise(n = n()) %>% 
  left_join(wpre.data %>% 
              filter(region != "Wedge" & batch == 'batch_2') %>% 
              group_by(age) %>% 
              summarize(age_n = n(), by = "age")) %>%
  mutate(pct = n/age_n)
```



```{r}
Head$tdtom <- wpre.data$tdtom

Head@meta.data %>% 
  rownames_to_column('bc') %>% 
  left_join(wpre.data %>% rownames_to_column('bc') %>% select(bc, tdtom), by = 'bc') %>% 
column_to_rownames('bc')

Head@meta.data[, "b_type"] <- "b2"
Head@meta.data[c(wpre_cells_batch2, wpre_cells_batch3, wpre_neighbors_batch2, wpre_neighbors_batch3),"b_type"] <- "b1"

mDimPlot(Head, group.by = "b_type", shuffle = T) 
mDimPlot(Head, group.by = "b_type", split.by = "age") 
```

### Label transfer to uninfected batch
```{r}
Head@active.assay = "RNA"

non_infected_features <- rownames(Head)[!rownames(Head) %in% c('Wpre', 'GFP', 'Cre')]

Head_batch1 <- subset(Head, subset = batch == "batch_1", features = non_infected_features) %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20) %>%
  FindNeighbors(dims = 1:20)


Head_batch23 <- subset(Head, subset = batch %in% c("batch_2", "batch_3"))
Head_batch23 <- Head_batch23 %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA(assay = "RNA",
         npcs = 20)
Head_batch23 <- IntegrateLayers(Head_batch23, method = CCAIntegration, orig.reduction = "pca", dims = 1:20)

Head.anchors <- Head_batch23 %>%
  FindTransferAnchors(reference = .,
                      query = Head_batch1,
                      dims = 1:20,
                      reference.reduction = "integrated.dr")

predictions <- TransferData(anchorset = Head.anchors, refdata = Head_batch23$b_type, dims = 1:20)

inferredb1_batch1_cells <- predictions %>% filter(predicted.id != "b2") %>% rownames()
Head@meta.data[inferredb1_batch1_cells, "tdtom"] <- "TdTomato+ LT"
Head@meta.data[inferredb1_batch1_cells, "b_type"] <- "b1"

#---------------------------------

# Head@active.assay = "RNA"
# non_infected_features <- rownames(Head)[!rownames(Head) %in% c('Wpre', 'GFP', 'Cre')]
# 
# Head_batch1 <- Head %>% 
#   subset(subset = batch == "batch_1", features = non_infected_features) %>% 
#   NormalizeData() %>%
#   FindVariableFeatures() %>%
#   ScaleData() %>% 
#   RunPCA(assay = "RNA", 
#          npcs = 20) %>% 
#   FindNeighbors(dims = 1:20)
# 
# 
# Head_batch23 <- Head %>% subset(subset = batch != "batch_1", features = non_infected_features)
# Head_batch23 <- JoinLayers(Head_batch23, assay = "RNA")
# Head_batch23[["RNA"]] <- split(Head_batch23[["RNA"]], f = Head_batch23$batch)
# 
# Head_batch23 <- Head_batch23 %>% 
#   NormalizeData() %>%
#   FindVariableFeatures() %>%
#   ScaleData() %>% 
#   RunPCA(assay = "RNA", 
#          npcs = 20)
# Head_batch23 <- IntegrateLayers(Head_batch23, method = CCAIntegration, orig.reduction = "pca", dims = 1:20)
# 
# Head.anchors <- Head_batch23 %>% 
#   FindTransferAnchors(reference = ., 
#                       query = Head_batch1, 
#                       dims = 1:20,
#                       reference.reduction = "pca")
# 
# predictions <- TransferData(anchorset = Head.anchors, 
#                             refdata = Head_batch23$b_type, 
#                             dims = 1:20)
# 
# predictions %>% filter(predicted.id =='b1')
# 
# inferredb1_batch1_cells <- predictions %>% filter(predicted.id == "b1") %>% rownames()
# Head@meta.data[inferredb1_batch1_cells, "tdtom"] <- "TdTomato+ LT"
# Head@meta.data[inferredb1_batch1_cells, "b_type"] <- "b1"
```


```{r}
Head@active.assay <- 'SCT'

mFeaturePlot(Head, features = c("S100a6", "Egfr", "Crym", "Urah", "Tfap2c"), order = T, ncol = 5, legend = F) 

mDimPlot(Head, group.by = "age",  order = T) + 
  scale_color_manual(values = c('grey90', 'tomato'))

mDimPlot(Head, group.by = "activation_state", split.by = "age", shuffle = T) + 
  scale_color_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') 

mFeaturePlot(Head, features = c("Crym", "Egfr"), split.by = "age",  order = T) 

mDimPlot(Head, group.by = "b_type",  shuffle = T) + 
  scale_color_manual(values = c("b1" = "skyblue1", "b2" = "mediumblue"), labels = c('B1', 'B2'))

mDimPlot(Head, group.by = "b_type", split.by = "age", shuffle = T) + 
  scale_color_manual(values = c("b1" = "skyblue1", "b2" = "mediumblue"), labels = c('B1', 'B2'))

mDimPlot(Head, group.by = "tdtom", order = T) + 
  scale_color_manual(values = c("TdTomato+" = "magenta", 
                                "TdTomato+ NN" = "darkorange", 
                                "TdTomato+ LT" = 'darkgreen'),
                     na.value = 'grey90')

mDimPlot(Head, group.by = "tdtom", split.by = 'tdtom', order = T) + 
  scale_color_manual(values = c("TdTomato+" = "magenta", 
                                "TdTomato+ NN" = "darkorange", 
                                "TdTomato+ LT" = 'darkgreen'),
                     na.value = 'grey90')
``` 

```{r}
mDimPlot(Head, group.by = "SCT_snn_res.1",  shuffle = T, label = T, legend = F)
mDimPlot(Head, group.by = "SCT_snn_res.1",  split.by = 'region', shuffle = T, label = T, legend = F)
``` 

```{r}
Head@meta.data %>% 
  ggplot(aes(x = age, fill = b_type)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_fill_manual(values = c("b1" = "skyblue1", "b2" = "mediumblue"), labels = c('B1', 'B2')) +
  scale_y_continuous(labels = scales::percent, expand = c(0,0)) +
  labs(x = "Age", y = "Fraction of cells", fill = "B cell type") 
ggsave('../figures/b1b2_barplot.pdf', width = 5, height = 3)
```


# -Activation/Quiescence
##  in B1 and B2 cells
```{r}
a <- Head@meta.data %>% 
  filter(region == 'LW' & age == 'p30') %>% 
  ggplot(aes(x = b_type, fill = activation_state)) +
  geom_bar() +
  theme_classic() + 
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_discrete(labels = c('B1', 'B2')) + 
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') +
  labs(x = NULL, y = 'Count')

b <- Head@meta.data %>% 
  filter(region == 'LW' & age == 'p30') %>% 
  ggplot(aes(x = b_type, fill = activation_state)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
  scale_x_discrete(labels = c('B1', 'B2')) +  
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') +
  labs(x = NULL, y = 'Share')

a + b + plot_layout(guides = 'collect') + plot_annotation(caption = 'Only cells in the LW region at p30 are shown.')
```

## in p30 and p365
```{r}
c <- Head@meta.data %>% 
  filter(region == 'LW') %>% 
  ggplot(aes(x = age, fill = activation_state)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_manual(values = c('#1c9099', '#a6bddb'), name = 'Activation State') +
  labs(x = NULL, y = 'Share')

b + c + plot_layout(guides = 'collect', axis_titles = 'collect_y') + plot_annotation(caption = 'Only cells in the LW region are shown.')
ggsave("../figures/activation_states.pdf", width = 5, height = 3)
```


#- DEGs
```{r}
Idents(Head) <- "b_type"

HeadQ <- Head %>% subset(subset = activation_state == 'quiescent') %>% 
  SCTransform(return.only.var.genes = F) %>% 
  PrepSCTFindMarkers()

btype_markers <- FindAllMarkers(HeadQ, 
                                logfc.threshold = 0, 
                                min.pct = 0, 
                                only.pos = T)

btype_markers <- btype_markers %>%
  group_by(cluster) %>%
  mutate(rank = rank(p_val_adj, ties.method = "first")) %>%
  arrange(cluster, rank)
```


#- Volcano Plot
```{r}
validated.genes = c("Atf3", "Ptprz1", "Riiad1", "FoxJ1", "Gadd45b", "Zeb1", "Tagln2", "Emp1", "Anxa2")
regional.genes = c("Crym", "Nrg1", "Klf2", "Cebpd", "Gm29260", "Pax6", "Rlbp1", "Nkx6-2")
b1_degenes = btype_markers %>% filter(cluster == "b1") %>% rownames()
b2_degenes = btype_markers %>% filter(cluster == "b2") %>% rownames()
genes_to_highlight = c(b1_degenes[1:5], b2_degenes[1:5],validated.genes, regional.genes)

volcano.data = btype_markers %>% 
                mutate(labs = if_else(gene %in% genes_to_highlight, gene, "")) %>% 
                mutate(significant = case_when(p_val_adj < 0.05 & 
                                               cluster == "b1" ~ "b1",
                                               p_val_adj < 0.05 & cluster == "b2" ~ "b2",
                                               TRUE ~ "Not differentially expressed")) %>% 
                mutate(significant = if_else(gene%in%regional.genes, "regional.genes", significant)) %>%
                mutate(log2FC = case_when(cluster == "b2" ~ avg_log2FC*-1,
                                          TRUE ~ avg_log2FC)) %>% 
                mutate(significant = if_else(gene%in%validated.genes, "Validated gene", significant)) %>%
                mutate(priority = gene %in% genes_to_highlight) %>%
                arrange(priority, gene) %>%
                select(-priority) 
                
  volcano.data %>% 
  ggplot(aes(log2FC, -log(base = 10, p_val_adj),  label = labs)) + 
  geom_point(aes(col = significant), stroke = 0, size = 4, alpha = 0.5) + 
  scale_color_manual(name = NULL, labels = c("Upregulated",
                                             "Downregulated",
                                             "Not differentially expressed",
                                             "Regionalization-associated genes",
                                             "Validated gene"
                                             ), values = c("skyblue1", "mediumblue", "grey75","darkgreen","magenta")) +
  geom_text_repel(max.overlaps = Inf, size=5, box.padding=0.3) +
  theme_classic() +
  theme(panel.grid.minor = element_blank(), 
        text = element_text(family = "Helvetica", size = 15)
        ) + 
  labs(title = "Differential expression between B1 and B2 cells",
       x = "Average log2FC",
       y = "-log10 adjusted P value")
  
ggsave("data/btype_volcano.pdf", width = 6, height = 4)
```
#-Cell Cycle Score
```{r}
Head@active.assay <- 'SCT'
s.genes <- cc.genes$s.genes %>% str_to_title()
g2m.genes <- cc.genes$g2m.genes %>% str_to_title()

Head <- CellCycleScoring(Head, s.features = s.genes, g2m.features = g2m.genes)

mDimPlot(Head, group.by = 'Phase')
```
```{r}
phaseplot1 <- Head@meta.data %>% 
  filter(region == 'LW') %>% 
  ggplot(aes(x = b_type, fill = Phase)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_y_continuous(name = 'Share', expand = c(0, 0), labels = scales::percent) +
  scale_x_discrete(labels = c('B1', 'B2')) + 
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_brewer(type = 'seq', palette = 8) + 
  labs(x = NULL, y = 'Count')

phaseplot2 <- Head@meta.data %>% 
  filter(region == 'LW') %>%
  ggplot(aes(x = age, fill = Phase)) +
  geom_bar(position = 'fill') +
  theme_classic() + 
  scale_y_continuous(name = 'Share', expand = c(0, 0), labels = scales::percent) +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + 
  scale_fill_brewer(type = 'seq', palette = 8) + 
  labs(x = NULL, y = 'Count')

phaseplot1 + phaseplot2 + plot_layout(guides = 'collect', axis_titles = 'collect_y') + plot_annotation(caption = 'Only cells in the LW region are shown.')
ggsave("../figures/cell_cycle_phases.pdf", width = 5, height = 3)
```


# -B1 score 
```{r}
Head@active.assay <- "SCT"
b.markers.list = list(b1 = c("Atf3", "Riiad1", "Foxj1", "Gadd45b", "Tagln2", "Emp1"))

Head = AddModuleScore(Head, features = b.markers.list)
Head@meta.data$B1_score <- NULL 
Head@meta.data = Head@meta.data %>% dplyr::rename(B1_score = Cluster1)

#Re-scaling Module Scores:These lines rescale the module scores (B1_score and B2_score) so that they have a mean of 0 and a standard deviation of 1. This step standardizes the scores, making them comparable across different modules.

Head$B1_score = Head$B1_score %>% rescale() 

mFeaturePlot(Head, features = "B1_score", order = T) & 
  scale_color_distiller(type = 'seq', palette = 16, direction = 1)

saveRDS(Head, "data/Head_withp365.rds")
```

```{r}
mFeaturePlot(Head, features = c('Atf3', 'Tagln2', 'Emp1'), order = T, ncol = 3, legend = F)

mFeaturePlot(HeadQ, 
             features = b.markers.list$b1, 
             split.by = 'b_type', 
             order = T,
             legend = F)

DotPlot(HeadQ, b.markers.list$b1, group.by = 'b_type', scale = F )
```

```{r}
Head@meta.data %>% filter(activation_state == 'quiescent') %>% 
  ggplot(aes(x = age, y = B1_score, color = b_type)) +
  geom_jitter(height = 0, alpha = 0.2, size = 0.5) +
  geom_boxplot(outliers = F, fill = NA, size = 0.8) +
  theme_classic() +
  scale_y_continuous() +
  scale_color_manual(name = 'B Type', 
                     values = c('b1' = 'skyblue', 'b2' = 'mediumblue'), 
                     labels = c('B1', 'B2')) + 
  labs(title = 'B1 Score in quiescent B cells', x = 'Age', y = 'B1 Score') +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5))

ggsave('../figures/b1_scores_by_age.pdf', height = 3.5, width = 4.5)
```  
#-Heatmap

## Colors
```{r}
#Main color
col_values <- seq(-2, 2, 0.5)
b1_score_values <- seq(0, 1, 0.125)
col_fun <- colorRamp2(c(-2, 0, 2), c("cyan", "white", "magenta"))

#annotations colors
top_anno_colors <- list(Dissection = c("LV" = "grey90", "LW" = "purple", "Wedge" = "forestgreen"),
                         `B Cell type` = c("b1" = 'skyblue1', "b2" = "mediumblue"),
                        `B1 Score` = colorRamp2(b1_score_values, brewer.pal(n = length(b1_score_values), name = "YlGnBu")))
```


## Data
```{r}
set.seed(123)
btype_markers$cluster <- btype_markers$cluster %>% fct_rev()

heatmap_genes <- btype_markers %>%
  group_by(cluster) %>%
  top_n(100, dplyr::desc(p_val_adj)) %>%
  arrange(cluster) %>%
  pull(gene)

heatmap_cells <- Head@meta.data %>% 
  rownames_to_column('cell_barcode') %>%
  group_by(b_type) %>%
  sample_n(300) %>%
  pull(cell_barcode)

scaled_data <- Head@assays$SCT$scale.data %>% as.data.frame() 
heatmap_data <- scaled_data[heatmap_genes, heatmap_cells] %>% drop_na()
heatmap_metadata <- Head@meta.data[heatmap_cells,]


top_anno <- HeatmapAnnotation(Dissection    = heatmap_metadata$region,
                              `B Cell type` = heatmap_metadata$b_type,
                              `B1 Score`    = heatmap_metadata$B1_score,
                              col = top_anno_colors)



genes_to_highlight <- c('Atf3', 'Riiad1', 'Foxj1', 'Gadd45b', 'Emp1', 'Tagln2', 'Anxa2', 'Ptprz1', 'Tox', 'Thbs4', 'Aldoc', 'Zeb1', 'Mgef8' )
genes.index <- which(heatmap_genes %in% genes_to_highlight)
left_anno <- anno_mark(at = genes.index, labels = heatmap_genes[genes.index], which = "row", side = 'left')



hm <- rowAnnotation(right = left_anno) + 
      Heatmap(heatmap_data, 
              name = "Scaled Expression",
              top_annotation = top_anno,
              col = col_fun,
              #column_split = heatmap_metadata$b_type,
              column_order = heatmap_metadata %>% arrange(-B1_score) %>% rownames(),
              cluster_column_slices = F,
              border = T,
              cluster_rows = F,
              show_column_names = F,
              show_row_names = T,
              show_column_dend = F,
              column_title = NULL,
              show_row_dend = F,
              row_names_side = 'left') 

pdf("../figures/b1b2_degenes_heatmap.pdf", width = 8.2, height = 5.8)
hm
dev.off()
```

#P9+P30+P365  -------------------------------
```{r}
# Head <- subset(integrated_exp, cells = b_cells)
```

```{r}
Head <- Head %>% 
        SCTransform() %>% 
        RunPCA(assay = "SCT", npcs = 100) %>% 
        IntegrateLayers(method               = CCAIntegration, 
                        normalization.method = "SCT", 
                        verbose              = F) %>% 
        RunUMAP(dims = 1:100, reduction = "integrated.dr") %>% 
        FindNeighbors(reduction = "integrated.dr", dims = 1:100) %>% 
        FindClusters(resolution = seq(0.5, 2, 0.5), graph.name = "SCT_snn")

DimPlot(Head, label=T, shuffle = T, group.by ="SCT_snn_res.1") + 
  NoLegend()  + 
  coord_fixed() + 
  NoAxes()
